Preprints
https://doi.org/10.5194/nhess-2023-70
https://doi.org/10.5194/nhess-2023-70
05 May 2023
 | 05 May 2023
Status: this preprint is currently under review for the journal NHESS.

Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide susceptibility modeling

Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford

Abstract. Slope units are terrain partitions bounded by drainage and divide lines. They provide several advantages over gridded units in landslide-susceptibility modeling, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and polygon), and better accommodating the varying data accuracy and precision in landslide inventories. However, the use of slope units in regional (> 100 km2) landslide susceptibility studies remains limited due, in part, to prohibitive computational costs and/or poor reproducibility with current delineation methods. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units. The algorithm uses geomorphic scaling laws to define the appropriate scaling of the slope units representative of hillslope processes, avoiding the costly parameter optimization procedures of other slope unit delineation methods. We then demonstrate how slope units enable more robust regional-scale landslide susceptibility maps.

Jacob B. Woodard et al.

Status: open (until 16 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Jacob B. Woodard et al.

Jacob B. Woodard et al.

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Short summary
Dividing landscapes into representative hillslopes greatly improves predictions of landslide potential across landscapes but requires vast computing power. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units. The results of this work will allow an improved understanding of landslide potential across different landscapes and can ultimately help reduce the impacts of landslides worldwide.
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